curllm 1.0.43

curllm - Intelligent browser automation with local LLMs (Qwen, Llama, Mistral) for data extraction and form automation using a dynamic LLM-DSL architecture without hardcoded selectors.

AI Cost Tracking

• 🤖 LLM usage: $6.6683 (171 commits)
• 👤 Human dev: ~$7034 (70.3h @ $100/h, 30min dedup)

Generated on 2026-07-05 using openrouter/qwen/qwen3-coder-next

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curllm = curl + LLM

Intelligent Browser Automation with Local LLMs

Quick Start • Features • Examples • Documentation • API

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🎯 What is curllm?

curllm is a powerful CLI tool that combines browser automation with local LLMs (like Ollama's Qwen, Llama, Mistral) to intelligently extract data, fill forms, and automate web workflows - all running locally on your machine with complete privacy.

🆕 v2 LLM-DSL Architecture! Dynamic element detection, semantic goal understanding, no hardcoded selectors. 388 tests passing.

# Extract products with prices from any e-commerce site
curllm "https://shop.example.com" -d "Find all products under $100"

# Fill contact forms automatically
curllm --stealth "https://example.com/contact" -d "Fill form: name=John, email=[e-mail]"

# Extract all emails from a page
curllm "https://example.com" -d "extract all email addresses"

✨ Features

Feature	Description
🧠 Local LLM	Works with 8GB GPUs (Qwen 2.5, Llama 3, Mistral)
🎯 Smart Extraction	LLM-guided DOM analysis - no hardcoded selectors
📝 Form Automation	Auto-fill forms with intelligent field mapping
🥷 Stealth Mode	Bypass anti-bot detection
👁️ Visual Mode	See browser actions in real-time
🔍 BQL Support	Browser Query Language for structured queries
📊 Export Formats	JSON, CSV, HTML, XLS output
🔒 Privacy-First	Everything runs locally - no cloud APIs needed

🧠 LLM-DSL Architecture

curllm v2 uses LLM-DSL (LLM Domain Specific Language) - a dynamic approach that eliminates hardcoded selectors:

┌─────────────────────────────────────────────────────────────┐
│                     LLM-DSL Flow                            │
├─────────────────────────────────────────────────────────────┤
│  1. Goal Detection (semantic)                               │
│     "Find RAM DDR5" → FIND_PRODUCTS                         │
│                                                             │
│  2. Strategy Selection                                      │
│     FIND_PRODUCTS → use search flow                         │
│     FIND_CART → find link by semantic scoring               │
│                                                             │
│  3. Element Finding (LLM-first)                             │
│     LLM analysis → Statistical scoring → Fallback           │
│                                                             │
│  4. Dynamic Selector Generation                             │
│     Analyze DOM → Score elements → Generate selector        │
└─────────────────────────────────────────────────────────────┘

Key Benefits

Feature	Traditional	LLM-DSL
Selectors	Hardcoded CSS/XPath	Dynamic generation
Keywords	Static lists	Semantic analysis
Language	English only	Multi-language (PL, EN)
Maintenance	Manual updates	Self-adapting

🚀 Quick Start

Installation

pip install -U curllm
curllm-setup      # One-time setup (installs Playwright browsers)
curllm-doctor     # Verify installation

Requirements

• Python 3.10+
• GPU: NVIDIA with 6-8GB VRAM (RTX 3060/4060) or CPU mode
• Ollama: For local LLM inference

# Install Ollama (if not installed)
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull qwen2.5:7b

📖 Examples

Extract Data

# Extract all links
curllm "https://example.com" -d "extract all links"

# Extract emails
curllm "https://example.com/contact" -d "extract all email addresses"
# Output: {"emails": ["[e-mail]", "[e-mail]"]}

# Extract products with price filter
curllm --stealth "https://shop.example.com" -d "Find all products under 500zł"

Form Automation

# Fill contact form
curllm --visual --stealth "https://example.com/contact" \
  -d "Fill form: name=John Doe, email=[e-mail], message=Hello"

# Login automation
curllm --visual "https://app.example.com/login" \
  -d '{"instruction":"Login", "credentials":{"user":"admin", "pass":"secret"}}'

Export Results

# Export to CSV
curllm "https://example.com" -d "extract all products" --csv -o products.csv

# Export to HTML
curllm "https://example.com" -d "extract all links" --html -o links.html

# Export to Excel
curllm "https://example.com" -d "extract all data" --xls -o data.xlsx

Screenshots

# Take screenshot
curllm "https://example.com" -d "screenshot"

# Visual mode (watch browser)
curllm --visual "https://example.com" -d "extract all links"

BQL Queries

curllm --bql -d 'query {
  page(url: "https://news.ycombinator.com") {
    title
    links: select(css: "a.titlelink") { text url: attr(name: "href") }
  }
}'

🌐 Web Interface

curllm-web start   # Start web UI at http://localhost:5000
curllm-web status  # Check status
curllm-web stop    # Stop server

Features:

• 🎨 Modern responsive UI
• 📝 19 pre-configured prompts
• 📊 Real-time log viewer
• 📤 File upload support

🔧 Configuration

Environment variables (.env):

CURLLM_MODEL=qwen2.5:7b          # LLM model
CURLLM_OLLAMA_HOST=http://localhost:11434
CURLLM_HEADLESS=true             # Run browser headlessly
CURLLM_STEALTH_MODE=false        # Anti-detection
CURLLM_LOCALE=en-US              # Browser locale

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         curllm CLI                              │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌────────────────┐    ┌────────────────┐    ┌───────────────┐  │
│  │  DSL Executor  │───▶│ Knowledge Base │───▶│ Strategy YAML │  │
│  │  (Orchestrator)│    │   (SQLite)     │    │    Files      │  │
│  └────────────────┘    └────────────────┘    └───────────────┘  │
│          │                                                      │
│          ▼                                                      │
│  ┌────────────────────────────────────────────────────────────┐ │
│  │                    DOM Toolkit (Pure JS)                   │ │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌────────────┐  │ │
│  │  │Structure │  │ Patterns │  │Selectors │  │   Prices   │  │ │
│  │  │ Analyzer │  │ Detector │  │Generator │  │  Detector  │  │ │
│  │  └──────────┘  └──────────┘  └──────────┘  └────────────┘  │ │
│  └────────────────────────────────────────────────────────────┘ │
│          │                                                      │
│          ▼                                                      │
│  ┌────────────────────────────────────────────────────────────┐ │
│  │              Playwright Browser Engine                     │ │
│  │         (Chromium with Stealth & Anti-Detection)           │ │
│  └────────────────────────────────────────────────────────────┘ │
│          │                                                      │
│          ▼                                                      │
│  ┌────────────────────────────────────────────────────────────┐ │
│  │                 Ollama / LiteLLM                           │ │
│  │      (Local LLM: Qwen 2.5, Llama 3, Mistral, GPT, etc)     │ │
│  └────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Key Components

Component	Description	LLM Calls
URL Resolver	Smart navigation with goal detection	0-1
Goal Detector	Semantic intent understanding	0-1
Element Finder	Dynamic selector generation	0-1
DOM Toolkit	Pure JavaScript atomic queries	0
SPA Hydration	Wait for CSR/SPA content	0

📖 Full Architecture Documentation →

🧬 DSL System (Strategy-Based Extraction)

Note: The YAML DSL system works alongside the newer LLM-DSL. YAML strategies are used for known sites with proven extraction patterns, while LLM-DSL handles unknown sites dynamically.

curllm automatically learns and saves successful extraction strategies as YAML files:

# dsl/ceneo_products.yaml - Auto-generated from successful extraction
url_pattern: "*.ceneo.pl/*"
task: extract_products
algorithm: statistical_containers

selector: div.product-card
fields:
  name: h3.title
  price: span.price
  url: a[href]

metadata:
  success_rate: 0.95
  use_count: 42

How It Works

1. First visit - LLM-DSL dynamically analyzes page, extracts data
2. Successful - Strategy saved to dsl/*.yaml, recorded in Knowledge Base
3. Next visit - Knowledge Base loads saved strategy (fast path)
4. Unknown site - Falls back to LLM-DSL dynamic discovery

┌─────────────────────────────────────────────────────────┐
│                   Request Flow                          │
├─────────────────────────────────────────────────────────┤
│  URL → Knowledge Base lookup                            │
│        │                                                │
│        ├─ Found? → Load YAML strategy (fast)            │
│        │                                                │
│        └─ Not found? → LLM-DSL dynamic (flexible)       │
│                        │                                │
│                        └─ Success? → Save to YAML       │
└─────────────────────────────────────────────────────────┘

Algorithms

Algorithm	Best For	Speed
statistical_containers	Product grids	⚡ Fast
pattern_detection	Lists, tables	⚡ Fast
llm_guided	Complex layouts	🐢 Slower
form_fill	Contact forms	⚡ Fast

📖 DSL System Documentation →

🤝 Multi-Provider LLM Support

curllm supports multiple LLM providers via LiteLLM:

from curllm_core import LLMConfig

# OpenAI
config = LLMConfig(provider="openai/gpt-4o-mini")

# Anthropic
config = LLMConfig(provider="anthropic/claude-3-haiku-20240307")

# Google Gemini
config = LLMConfig(provider="gemini/gemini-2.0-flash")

# Local Ollama (default)
config = LLMConfig(provider="ollama/qwen2.5:7b")

📚 Documentation

Getting Started

• 📖 Full Documentation
• ⚙️ Installation Guide
• 📝 Examples & Tutorials

Architecture

• 🏗️ System Architecture
• 🧬 DSL System - Strategy-based extraction
• ⚛️ DOM Toolkit - Pure JS queries
• 🧩 Components - Module overview
• 🔗 LLM-DSL URL Resolution - Smart URL navigation

Reference

• 🔌 API Reference
• 🤖 MCP (agents) — curllm-mcp for Cursor / Claude Desktop
• 🛠️ Configuration
• ❓ Troubleshooting

🧪 Development

# Clone and install
git clone https://github.com/wronai/curllm.git
cd curllm
make install

# Run tests (388 tests passing)
make test

# Run URL resolver examples
cd examples/url_resolver && python run_all.py

# Run with Docker
docker compose up -d

📄 License

Apache License 2.0 - see LICENSE

🙏 Acknowledgments

Built with:

• Playwright - Browser automation
• Ollama - Local LLM inference
• LiteLLM - Multi-provider LLM support
• Flask - Web framework

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Made with ❤️ by wronai

License

Licensed under Apache-2.0.